While on assignment, I was working with my team on a use case that combined IoT and surveillance. At a high level, we needed to implement a platform that could ingest, report and store vast amounts of data collected in real time. It circled real-time event detection and sensor alerting. The data was geographically distributed, with connectivity not always reliable. Retention policies thresholds had to be taken into consideration as well. Not only did storage strategies need to be factored in, but video-on-demand (VOD) was mandatory.
Streaming data back to the central location was not acceptable as this would have saturated the backhaul, blocked other data delivery, and in the end, ruin the customer experience. If you never saw churn before, this would be your one stop ticket to bankruptcy.
We decided to run analytics and detection algorithms on the edge, while storing results local to the region (remote location). The metadata and required artifacts would be transported back to headquarters for further analysis. This approach met the requirements, but a question lingered, would this approach meet future needs? Leverage the cloud as needed, keep data local to the region, process on the edge and send back necessary data for later viewing and querying of the remote locations.
After reading how IBM and Cisco were advancing to the edge for certain analytic use cases:
“This powerful IoT technology from Cisco and IBM, combined with Bell’s world leading network technology, enables customers to tap into innovative real-time analytics options to maximize performance across their operations, no matter where they are,”
Deploying the unmatched analytics capabilities of IBM Watson Internet of Things and Cisco networking intelligence with streaming edge analytics will help to further accelerate Bell’s leadership in Canadian IoT.” (ref.)
This solidified the solution and revealed that others are and will face similar types of issues, particularly when limited connectivity exists.
IBM pointed out that today there are billions of connected devices and sensors gathering vast amounts of real-time data and cloud has made it possible to gather valuable insight. But without high-bandwidth connectivity much of this insight goes missing or can’t be acted upon in real-time. (ref.)
As use cases for IoT continue to drive the industry forward, so will the need for real-time interaction with this data. Just as Kafka became the standard for data pipelines, hybrid cloud solutions will become the model for IoT platforms.
After eliminating the impossible task of bringing high-bandwidth connections to these locations, what remained was the probable technique of bringing analytics to the remote locations themselves. To put it quite simply, the team is looking to perform analytic computations at the point or edge of data collection. (ref.)
While the cloud has its place, just like Big Data, it is not the one solution for all. The edge will become a key factor, and with these hybrid solutions like “IBM Watson IoT,” they will become embedded within the world of IoT.
“With the vast amount of data being created at the edge of the network, using existing Cisco infrastructure to perform streaming analytics is the perfect way to cost-effectively obtain real-time insights…
Our powerful technology provides customers with the flexibility to combine this edge processing with the cognitive computing power of the IBM Watson IoT platform,” Anand said.” (ref.)